Commonsense Metaphysics and Lexical Semantics

In the TACITUS project for using commonsense knowledge in the understanding of texts •bout mechanical devices and their failures, we have been developing various commonsense theories that are needed to mediate between the way we talk about the behavior of such devices and causal models of their operation. Of central importance in this effort is the axiomatization of what might be called "commonsense metaphysics'. This includes a number of areas that figure in virtually every domain of discourse, such as scalar notions, granularity, time, space, material, physical objects, causality, functionality, force, and shape. Our approach to lexical semantics is then to construct core theories of each of these areas, and then to define, or at least characterize, • large number of lexical items in terms provided by the core theories. In the TACITUS system, processes for solving pragrnatics problems posed by • text will use the knowledge base consisting of these theories in conjunction with the logical forms of the sentences in the text to produce an interpretation. In this paper we do not stress these interpretation processes; this is another, important aspect of the TACITUS project, and it will be described in subsequent papers. This work represents a convergence of research in lexical semantics in linguistics and efforts in AI to encode commonsense knowledge. Lexical semanticist* over the years have developed formalisms of increasing adequacy for encoding word meaning, progressing from simple sets of features (Katz and Fodor, 1963) to notations for predicateargument structure (Lakoff, 1972; Miller and JohnsonLaird, 1976), but the early attempts still limited to world knowledge and assumed only very restricted sorts of processing. Workers in computational linguistics introduced inference (Rieger, 1974; Schank, 1975) and other complex cognitive processes (Herskovits, 1982) into our understanding of the role of word meaning. Recently, linguists have given greater attention to the cognitive processes that would operate on their representations (e.g., Talmy, 1983; Croft, 1986). Independently, in AI an effort arose to encode large amounts of commonsense knowledge (Hayes, 1979; Hobbs and Moore, 1985; Hobbs et al. 1985). The research reported here represents a convergence of these various developments. By developing core theories of several fundamental phenomena and defining lexical items within these theories, using the full power of predicate calculus, we awe able to cope with complexSties of word meaning that have hitherto escaped lexical semanticists, within a framework that gives full scope to the planning and reasoning processes that manipulate representations of word meaning.